CO2 Emissions — Automated EDA

Brought to you by YData

Dataset statistics

Number of variables	12
Number of observations	7385
Missing cells	295
Missing cells (%)	0.3%
Duplicate rows	1033
Duplicate rows (%)	14.0%
Total size in memory	2.3 MiB
Average record size in memory	322.6 B

Variable types

Categorical	3
Text	1
Numeric	8

Alerts

Dataset has 1033 (14.0%) duplicate rows	Duplicates
`co2_emissions_g_km_` is highly overall correlated with `cylinders` and 5 other fields	High correlation
`cylinders` is highly overall correlated with `co2_emissions_g_km_` and 6 other fields	High correlation
`engine_size_l_` is highly overall correlated with `co2_emissions_g_km_` and 5 other fields	High correlation
`fuel_consumption_city_l_100_km_` is highly overall correlated with `co2_emissions_g_km_` and 5 other fields	High correlation
`fuel_consumption_comb_l_100_km_` is highly overall correlated with `co2_emissions_g_km_` and 5 other fields	High correlation
`fuel_consumption_comb_mpg_` is highly overall correlated with `co2_emissions_g_km_` and 5 other fields	High correlation
`fuel_consumption_hwy_l_100_km_` is highly overall correlated with `co2_emissions_g_km_` and 5 other fields	High correlation
`make` is highly overall correlated with `cylinders`	High correlation
`transmission` has 295 (4.0%) missing values	Missing

Reproduction

Analysis started	2025-08-18 09:17:32.020899
Analysis finished	2025-08-18 09:17:37.312745
Duration	5.29 seconds
Software version	ydata-profiling vv4.16.1
Download configuration	config.json

make
Categorical

High correlation

Distinct	42
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	455.5 KiB

FORD	628
CHEVROLET	588
BMW	527
MERCEDES-BENZ	419
PORSCHE	376
Other values (37)	4847

Length

Max length	13
Median length	11
Mean length	6.1439404
Min length	3

Characters and Unicode

Total characters	45373
Distinct characters	27
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	ACURA
2nd row	ACURA
3rd row	ACURA
4th row	ACURA
5th row	ACURA

Common Values

Value	Count	Frequency (%)
FORD	628	8.5%
CHEVROLET	588	8.0%
BMW	527	7.1%
MERCEDES-BENZ	419	5.7%
PORSCHE	376	5.1%
TOYOTA	330	4.5%
GMC	328	4.4%
AUDI	286	3.9%
NISSAN	259	3.5%
JEEP	251	3.4%
Other values (32)	3393	45.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ford	628	8.3%
chevrolet	588	7.8%
bmw	527	7.0%
mercedes-benz	419	5.6%
porsche	376	5.0%
toyota	330	4.4%
gmc	328	4.3%
audi	286	3.8%
nissan	259	3.4%
jeep	251	3.3%
Other values (35)	3555	47.1%

Most occurring characters

Value	Count	Frequency (%)
E	4807	10.6%
O	3608	8.0%
A	3589	7.9%
R	3114	6.9%
I	2781	6.1%
D	2672	5.9%
N	2483	5.5%
C	2458	5.4%
S	2345	5.2%
M	2036	4.5%
Other values (17)	15480	34.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	45373	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	4807	10.6%
O	3608	8.0%
A	3589	7.9%
R	3114	6.9%
I	2781	6.1%
D	2672	5.9%
N	2483	5.5%
C	2458	5.4%
S	2345	5.2%
M	2036	4.5%
Other values (17)	15480	34.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	45373	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	4807	10.6%
O	3608	8.0%
A	3589	7.9%
R	3114	6.9%
I	2781	6.1%
D	2672	5.9%
N	2483	5.5%
C	2458	5.4%
S	2345	5.2%
M	2036	4.5%
Other values (17)	15480	34.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	45373	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	4807	10.6%
O	3608	8.0%
A	3589	7.9%
R	3114	6.9%
I	2781	6.1%
D	2672	5.9%
N	2483	5.5%
C	2458	5.4%
S	2345	5.2%
M	2036	4.5%
Other values (17)	15480	34.1%

model
Text

Distinct	2053
Distinct (%)	27.8%
Missing	0
Missing (%)	0.0%
Memory size	496.5 KiB

Length

Max length	41
Median length	32
Mean length	11.831957
Min length	2

Characters and Unicode

Total characters	87379
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	502 ?
Unique (%)	6.8%

Sample

1st row	ILX
2nd row	ILX
3rd row	ILX HYBRID
4th row	MDX 4WD
5th row	RDX AWD

Value	Count	Frequency (%)
awd	1128	6.8%
ffv	592	3.6%
4wd	477	2.9%
coupe	375	2.3%
4x4	333	2.0%
s	326	2.0%
4matic	239	1.4%
cabriolet	221	1.3%
xdrive	215	1.3%
cooper	204	1.2%
Other values (709)	12464	75.2%

Most occurring characters

Value	Count	Frequency (%)
	9200	10.5%
A	5815	6.7%
R	4636	5.3%
E	4496	5.1%
C	3621	4.1%
T	3510	4.0%
O	3450	3.9%
D	3182	3.6%
S	3165	3.6%
I	2352	2.7%
Other values (59)	43952	50.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	87379	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	9200	10.5%
A	5815	6.7%
R	4636	5.3%
E	4496	5.1%
C	3621	4.1%
T	3510	4.0%
O	3450	3.9%
D	3182	3.6%
S	3165	3.6%
I	2352	2.7%
Other values (59)	43952	50.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	87379	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	9200	10.5%
A	5815	6.7%
R	4636	5.3%
E	4496	5.1%
C	3621	4.1%
T	3510	4.0%
O	3450	3.9%
D	3182	3.6%
S	3165	3.6%
I	2352	2.7%
Other values (59)	43952	50.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	87379	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	9200	10.5%
A	5815	6.7%
R	4636	5.3%
E	4496	5.1%
C	3621	4.1%
T	3510	4.0%
O	3450	3.9%
D	3182	3.6%
S	3165	3.6%
I	2352	2.7%
Other values (59)	43952	50.3%

vehicle_class
Categorical

Distinct	16
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	494.8 KiB

SUV - SMALL	1217
MID-SIZE	1133
COMPACT	1022
SUV - STANDARD	735
FULL-SIZE	639
Other values (11)	2639

Length

Max length	24
Median length	21
Mean length	11.587407
Min length	7

Characters and Unicode

Total characters	85573
Distinct characters	24
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	COMPACT
2nd row	COMPACT
3rd row	COMPACT
4th row	SUV - SMALL
5th row	SUV - SMALL

Common Values

Value	Count	Frequency (%)
SUV - SMALL	1217	16.5%
MID-SIZE	1133	15.3%
COMPACT	1022	13.8%
SUV - STANDARD	735	10.0%
FULL-SIZE	639	8.7%
SUBCOMPACT	606	8.2%
PICKUP TRUCK - STANDARD	538	7.3%
TWO-SEATER	460	6.2%
MINICOMPACT	326	4.4%
STATION WAGON - SMALL	252	3.4%
Other values (6)	457	6.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	3042	20.8%
suv	1952	13.3%
small	1628	11.1%
standard	1273	8.7%
mid-size	1186	8.1%
compact	1022	7.0%
pickup	697	4.8%
truck	697	4.8%
full-size	639	4.4%
subcompact	606	4.1%
Other values (11)	1883	12.9%

Most occurring characters

Value	Count	Frequency (%)
S	8335	9.7%
A	7531	8.8%
	7240	8.5%
C	5478	6.4%
T	5454	6.4%
-	5327	6.2%
M	5174	6.0%
I	4979	5.8%
L	4688	5.5%
U	4668	5.5%
Other values (14)	26699	31.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	85573	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	8335	9.7%
A	7531	8.8%
	7240	8.5%
C	5478	6.4%
T	5454	6.4%
-	5327	6.2%
M	5174	6.0%
I	4979	5.8%
L	4688	5.5%
U	4668	5.5%
Other values (14)	26699	31.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	85573	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	8335	9.7%
A	7531	8.8%
	7240	8.5%
C	5478	6.4%
T	5454	6.4%
-	5327	6.2%
M	5174	6.0%
I	4979	5.8%
L	4688	5.5%
U	4668	5.5%
Other values (14)	26699	31.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	85573	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	8335	9.7%
A	7531	8.8%
	7240	8.5%
C	5478	6.4%
T	5454	6.4%
-	5327	6.2%
M	5174	6.0%
I	4979	5.8%
L	4688	5.5%
U	4668	5.5%
Other values (14)	26699	31.2%

engine_size_l_
Real number (ℝ)

High correlation

Distinct	51
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.1600677

Minimum	0.9
Maximum	8.4
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	0.9
5-th percentile	1.5
Q1	2
median	3
Q3	3.7
95-th percentile	6
Maximum	8.4
Range	7.5
Interquartile range (IQR)	1.7

Descriptive statistics

Standard deviation	1.3541705
Coefficient of variation (CV)	0.42852577
Kurtosis	-0.13196328
Mean	3.1600677
Median Absolute Deviation (MAD)	1
Skewness	0.80918099
Sum	23337.1
Variance	1.8337776
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	1460	19.8%
3	804	10.9%
3.6	536	7.3%
3.5	529	7.2%
2.5	423	5.7%
2.4	346	4.7%
1.6	302	4.1%
5.3	290	3.9%
1.8	216	2.9%
1.4	211	2.9%
Other values (41)	2268	30.7%

Value	Count	Frequency (%)
0.9	3	< 0.1%
1	18	0.2%
1.2	25	0.3%
1.3	11	0.1%
1.4	211	2.9%
1.5	207	2.8%
1.6	302	4.1%
1.8	216	2.9%
2	1460	19.8%
2.1	5	0.1%

Value	Count	Frequency (%)
8.4	5	0.1%
8	3	< 0.1%
6.8	8	0.1%
6.7	25	0.3%
6.6	29	0.4%
6.5	18	0.2%
6.4	46	0.6%
6.3	3	< 0.1%
6.2	162	2.2%
6	94	1.3%

cylinders
Real number (ℝ)

High correlation

Distinct	8
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.6150305

Minimum	3
Maximum	16
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	3
5-th percentile	4
Q1	4
median	6
Q3	6
95-th percentile	8
Maximum	16
Range	13
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.8283065
Coefficient of variation (CV)	0.32560937
Kurtosis	1.525175
Mean	5.6150305
Median Absolute Deviation (MAD)	2
Skewness	1.1104154
Sum	41467
Variance	3.3427047
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
4	3220	43.6%
6	2446	33.1%
8	1402	19.0%
12	151	2.0%
3	95	1.3%
10	42	0.6%
5	26	0.4%
16	3	< 0.1%

Value	Count	Frequency (%)
3	95	1.3%
4	3220	43.6%
5	26	0.4%
6	2446	33.1%
8	1402	19.0%
10	42	0.6%
12	151	2.0%
16	3	< 0.1%

Value	Count	Frequency (%)
16	3	< 0.1%
12	151	2.0%
10	42	0.6%
8	1402	19.0%
6	2446	33.1%
5	26	0.4%
4	3220	43.6%
3	95	1.3%

transmission
Real number (ℝ)

Missing

Distinct	7
Distinct (%)	0.1%
Missing	295
Missing (%)	4.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.8866008

Minimum	4
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	4
5-th percentile	5
Q1	6
median	6
Q3	8
95-th percentile	9
Maximum	10
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.2092899
Coefficient of variation (CV)	0.17560041
Kurtosis	-0.26719351
Mean	6.8866008
Median Absolute Deviation (MAD)	1
Skewness	0.54552998
Sum	48826
Variance	1.4623822
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
6	3259	44.1%
8	1802	24.4%
7	1026	13.9%
9	419	5.7%
5	307	4.2%
10	210	2.8%
4	67	0.9%
(Missing)	295	4.0%

Value	Count	Frequency (%)
4	67	0.9%
5	307	4.2%
6	3259	44.1%
7	1026	13.9%
8	1802	24.4%
9	419	5.7%
10	210	2.8%

Value	Count	Frequency (%)
10	210	2.8%
9	419	5.7%
8	1802	24.4%
7	1026	13.9%
6	3259	44.1%
5	307	4.2%
4	67	0.9%

fuel_type
Categorical

Distinct	5
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	418.4 KiB

X	3637
Z	3202
E	370
D	175
N	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	7385
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	Z
2nd row	Z
3rd row	Z
4th row	Z
5th row	Z

Common Values

Value	Count	Frequency (%)
X	3637	49.2%
Z	3202	43.4%
E	370	5.0%
D	175	2.4%
N	1	< 0.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
x	3637	49.2%
z	3202	43.4%
e	370	5.0%
d	175	2.4%
n	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
X	3637	49.2%
Z	3202	43.4%
E	370	5.0%
D	175	2.4%
N	1	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7385	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
X	3637	49.2%
Z	3202	43.4%
E	370	5.0%
D	175	2.4%
N	1	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7385	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
X	3637	49.2%
Z	3202	43.4%
E	370	5.0%
D	175	2.4%
N	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7385	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
X	3637	49.2%
Z	3202	43.4%
E	370	5.0%
D	175	2.4%
N	1	< 0.1%

fuel_consumption_city_l_100_km_
Real number (ℝ)

High correlation

Distinct	211
Distinct (%)	2.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.556534

Minimum	4.2
Maximum	30.6
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	4.2
5-th percentile	8
Q1	10.1
median	12.1
Q3	14.6
95-th percentile	19.2
Maximum	30.6
Range	26.4
Interquartile range (IQR)	4.5

Descriptive statistics

Standard deviation	3.5002741
Coefficient of variation (CV)	0.27876118
Kurtosis	1.196145
Mean	12.556534
Median Absolute Deviation (MAD)	2.2
Skewness	0.80900471
Sum	92730
Variance	12.251919
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10.8	125	1.7%
12.4	123	1.7%
11.8	120	1.6%
11.9	119	1.6%
10.6	109	1.5%
10.2	108	1.5%
11.3	107	1.4%
10.5	104	1.4%
12.1	104	1.4%
11.2	102	1.4%
Other values (201)	6264	84.8%

Value	Count	Frequency (%)
4.2	5	0.1%
4.3	4	0.1%
4.4	5	0.1%
4.5	8	0.1%
4.6	9	0.1%
4.7	3	< 0.1%
4.8	3	< 0.1%
4.9	8	0.1%
5	4	0.1%
5.1	4	0.1%

Value	Count	Frequency (%)
30.6	2	< 0.1%
30.3	2	< 0.1%
30.2	2	< 0.1%
30	2	< 0.1%
26.8	3	< 0.1%
26.7	1	< 0.1%
26.6	2	< 0.1%
26.3	1	< 0.1%
26.2	1	< 0.1%
25.7	2	< 0.1%

fuel_consumption_hwy_l_100_km_
Real number (ℝ)

High correlation

Distinct	143
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.0417062

Minimum	4
Maximum	20.6
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	4
5-th percentile	6.1
Q1	7.5
median	8.7
Q3	10.2
95-th percentile	13.2
Maximum	20.6
Range	16.6
Interquartile range (IQR)	2.7

Descriptive statistics

Standard deviation	2.2244564
Coefficient of variation (CV)	0.24602175
Kurtosis	2.0089689
Mean	9.0417062
Median Absolute Deviation (MAD)	1.3
Skewness	1.0792167
Sum	66773
Variance	4.9482062
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7.8	209	2.8%
8.5	184	2.5%
8.7	169	2.3%
8.3	168	2.3%
8.4	162	2.2%
9.6	154	2.1%
7.7	152	2.1%
9.2	151	2.0%
7	150	2.0%
9	146	2.0%
Other values (133)	5740	77.7%

Value	Count	Frequency (%)
4	4	0.1%
4.2	1	< 0.1%
4.4	3	< 0.1%
4.5	2	< 0.1%
4.6	6	0.1%
4.7	1	< 0.1%
4.8	7	0.1%
4.9	10	0.1%
5	8	0.1%
5.1	16	0.2%

Value	Count	Frequency (%)
20.6	2	< 0.1%
20.5	5	0.1%
20.4	2	< 0.1%
20	1	< 0.1%
19.6	1	< 0.1%
19.3	4	0.1%
19.2	1	< 0.1%
18.8	2	< 0.1%
18.6	1	< 0.1%
18.5	5	0.1%

fuel_consumption_comb_l_100_km_
Real number (ℝ)

High correlation

Distinct	181
Distinct (%)	2.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.975071

Minimum	4.1
Maximum	26.1
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	4.1
5-th percentile	7.2
Q1	8.9
median	10.6
Q3	12.6
95-th percentile	16.5
Maximum	26.1
Range	22
Interquartile range (IQR)	3.7

Descriptive statistics

Standard deviation	2.8925063
Coefficient of variation (CV)	0.2635524
Kurtosis	1.3935754
Mean	10.975071
Median Absolute Deviation (MAD)	1.8
Skewness	0.89331572
Sum	81050.9
Variance	8.3665927
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
9.4	145	2.0%
8.4	136	1.8%
9.8	135	1.8%
9.1	132	1.8%
10.3	130	1.8%
8.7	128	1.7%
11	127	1.7%
9.9	125	1.7%
10.7	124	1.7%
9	121	1.6%
Other values (171)	6082	82.4%

Value	Count	Frequency (%)
4.1	4	0.1%
4.2	1	< 0.1%
4.3	2	< 0.1%
4.4	2	< 0.1%
4.5	5	0.1%
4.7	9	0.1%
4.8	7	0.1%
4.9	6	0.1%
5	5	0.1%
5.1	12	0.2%

Value	Count	Frequency (%)
26.1	2	< 0.1%
25.9	2	< 0.1%
25.8	2	< 0.1%
25.7	2	< 0.1%
23.9	1	< 0.1%
23	1	< 0.1%
22.6	4	0.1%
22.5	1	< 0.1%
22.2	3	< 0.1%
22.1	2	< 0.1%

fuel_consumption_comb_mpg_
Real number (ℝ)

High correlation

Distinct	54
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	27.481652

Minimum	11
Maximum	69
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	11
5-th percentile	17
Q1	22
median	27
Q3	32
95-th percentile	39
Maximum	69
Range	58
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	7.2318792
Coefficient of variation (CV)	0.263153
Kurtosis	2.499369
Mean	27.481652
Median Absolute Deviation (MAD)	5
Skewness	0.97703406
Sum	202952
Variance	52.300076
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25	482	6.5%
29	470	6.4%
27	461	6.2%
22	453	6.1%
26	445	6.0%
24	399	5.4%
23	359	4.9%
31	358	4.8%
30	333	4.5%
34	327	4.4%
Other values (44)	3298	44.7%

Value	Count	Frequency (%)
11	8	0.1%
12	6	0.1%
13	26	0.4%
14	30	0.4%
15	67	0.9%
16	134	1.8%
17	161	2.2%
18	159	2.2%
19	265	3.6%
20	311	4.2%

Value	Count	Frequency (%)
69	4	0.1%
67	1	< 0.1%
66	2	< 0.1%
64	2	< 0.1%
63	5	0.1%
60	9	0.1%
59	7	0.1%
58	6	0.1%
56	5	0.1%
55	12	0.2%

co2_emissions_g_km_
Real number (ℝ)

High correlation

Distinct	331
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	250.5847

Minimum	96
Maximum	522
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	57.8 KiB

Quantile statistics

Minimum	96
5-th percentile	169
Q1	208
median	246
Q3	288
95-th percentile	354
Maximum	522
Range	426
Interquartile range (IQR)	80

Descriptive statistics

Standard deviation	58.512679
Coefficient of variation (CV)	0.2335046
Kurtosis	0.47880085
Mean	250.5847
Median Absolute Deviation (MAD)	40
Skewness	0.52609381
Sum	1850568
Variance	3423.7336
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
242	85	1.2%
221	82	1.1%
230	77	1.0%
214	77	1.0%
294	76	1.0%
232	76	1.0%
258	75	1.0%
253	75	1.0%
246	75	1.0%
209	74	1.0%
Other values (321)	6613	89.5%

Value	Count	Frequency (%)
96	4	0.1%
99	1	< 0.1%
102	1	< 0.1%
103	1	< 0.1%
104	2	< 0.1%
105	3	< 0.1%
106	2	< 0.1%
108	2	< 0.1%
109	2	< 0.1%
110	7	0.1%

Value	Count	Frequency (%)
522	3	< 0.1%
493	2	< 0.1%
488	1	< 0.1%
487	1	< 0.1%
485	1	< 0.1%
476	1	< 0.1%
473	1	< 0.1%
467	1	< 0.1%
465	3	< 0.1%
464	2	< 0.1%

	co2_emissions_g_km_	cylinders	engine_size_l_	fuel_consumption_city_l_100_km_	fuel_consumption_comb_l_100_km_	fuel_consumption_comb_mpg_	fuel_consumption_hwy_l_100_km_	fuel_type	make	transmission	vehicle_class
co2_emissions_g_km_	1.000	0.852	0.869	0.956	0.963	-0.962	0.941	0.164	0.381	0.198	0.285
cylinders	0.852	1.000	0.936	0.847	0.834	-0.833	0.782	0.181	0.625	0.265	0.286
engine_size_l_	0.869	0.936	1.000	0.872	0.862	-0.861	0.816	0.242	0.499	0.218	0.262
fuel_consumption_city_l_100_km_	0.956	0.847	0.872	1.000	0.994	-0.993	0.949	0.325	0.355	0.172	0.291
fuel_consumption_comb_l_100_km_	0.963	0.834	0.862	0.994	1.000	-0.999	0.977	0.335	0.337	0.165	0.297
fuel_consumption_comb_mpg_	-0.962	-0.833	-0.861	-0.993	-0.999	1.000	-0.976	0.330	0.296	-0.164	0.262
fuel_consumption_hwy_l_100_km_	0.941	0.782	0.816	0.949	0.977	-0.976	1.000	0.332	0.277	0.140	0.311
fuel_type	0.164	0.181	0.242	0.325	0.335	0.330	0.332	1.000	0.447	0.270	0.296
make	0.381	0.625	0.499	0.355	0.337	0.296	0.277	0.447	1.000	0.474	0.359
transmission	0.198	0.265	0.218	0.172	0.165	-0.164	0.140	0.270	0.474	1.000	0.345
vehicle_class	0.285	0.286	0.262	0.291	0.297	0.262	0.311	0.296	0.359	0.345	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

	make	model	vehicle_class	engine_size_l_	cylinders	transmission	fuel_type	fuel_consumption_city_l_100_km_	fuel_consumption_hwy_l_100_km_	fuel_consumption_comb_l_100_km_	fuel_consumption_comb_mpg_	co2_emissions_g_km_
0	ACURA	ILX	COMPACT	2.0	4	5.0	Z	9.9	6.7	8.5	33	196
1	ACURA	ILX	COMPACT	2.4	4	6.0	Z	11.2	7.7	9.6	29	221
2	ACURA	ILX HYBRID	COMPACT	1.5	4	7.0	Z	6.0	5.8	5.9	48	136
3	ACURA	MDX 4WD	SUV - SMALL	3.5	6	6.0	Z	12.7	9.1	11.1	25	255
4	ACURA	RDX AWD	SUV - SMALL	3.5	6	6.0	Z	12.1	8.7	10.6	27	244
5	ACURA	RLX	MID-SIZE	3.5	6	6.0	Z	11.9	7.7	10.0	28	230
6	ACURA	TL	MID-SIZE	3.5	6	6.0	Z	11.8	8.1	10.1	28	232
7	ACURA	TL AWD	MID-SIZE	3.7	6	6.0	Z	12.8	9.0	11.1	25	255
8	ACURA	TL AWD	MID-SIZE	3.7	6	6.0	Z	13.4	9.5	11.6	24	267
9	ACURA	TSX	COMPACT	2.4	4	5.0	Z	10.6	7.5	9.2	31	212

	make	model	vehicle_class	engine_size_l_	cylinders	transmission	fuel_type	fuel_consumption_city_l_100_km_	fuel_consumption_hwy_l_100_km_	fuel_consumption_comb_l_100_km_	fuel_consumption_comb_mpg_	co2_emissions_g_km_
7375	VOLVO	S90 T6 AWD	MID-SIZE	2.0	4	8.0	Z	11.3	7.5	9.6	29	223
7376	VOLVO	V60 T5	STATION WAGON - SMALL	2.0	4	8.0	Z	10.5	7.1	8.9	32	208
7377	VOLVO	V60 T6 AWD	STATION WAGON - SMALL	2.0	4	8.0	Z	11.0	7.4	9.4	30	219
7378	VOLVO	V60 CC T5 AWD	STATION WAGON - SMALL	2.0	4	8.0	Z	10.8	7.7	9.4	30	220
7379	VOLVO	XC40 T4 AWD	SUV - SMALL	2.0	4	8.0	X	10.2	7.5	9.0	31	210
7380	VOLVO	XC40 T5 AWD	SUV - SMALL	2.0	4	8.0	Z	10.7	7.7	9.4	30	219
7381	VOLVO	XC60 T5 AWD	SUV - SMALL	2.0	4	8.0	Z	11.2	8.3	9.9	29	232
7382	VOLVO	XC60 T6 AWD	SUV - SMALL	2.0	4	8.0	Z	11.7	8.6	10.3	27	240
7383	VOLVO	XC90 T5 AWD	SUV - STANDARD	2.0	4	8.0	Z	11.2	8.3	9.9	29	232
7384	VOLVO	XC90 T6 AWD	SUV - STANDARD	2.0	4	8.0	Z	12.2	8.7	10.7	26	248

Most frequently occurring

	make	model	vehicle_class	engine_size_l_	cylinders	transmission	fuel_type	fuel_consumption_city_l_100_km_	fuel_consumption_hwy_l_100_km_	fuel_consumption_comb_l_100_km_	fuel_consumption_comb_mpg_	co2_emissions_g_km_	# duplicates
623	LEXUS	GS F	COMPACT	5.0	8	8.0	Z	14.9	9.7	12.5	23	293	5
197	CHRYSLER	300	FULL-SIZE	3.6	6	8.0	X	12.4	7.8	10.3	27	242	4
200	CHRYSLER	300 AWD	FULL-SIZE	3.6	6	8.0	X	12.8	8.7	11.0	26	258	4
286	FIAT	500L	STATION WAGON - SMALL	1.4	4	6.0	X	10.7	7.9	9.4	30	221	4
635	LEXUS	NX 300h AWD	SUV - SMALL	2.5	4	6.0	X	7.2	7.9	7.5	38	176	4
638	LEXUS	RC F	SUBCOMPACT	5.0	8	8.0	Z	15.2	9.5	12.6	22	289	4
640	LEXUS	RX 350 AWD	SUV - SMALL	3.5	6	8.0	X	12.2	9.0	10.8	26	252	4
643	LEXUS	RX 450h AWD	SUV - STANDARD	3.5	6	6.0	Z	7.5	8.4	7.9	36	185	4
794	MITSUBISHI	RVR 4WD	SUV - SMALL	2.0	4	6.0	X	10.1	8.2	9.2	31	213	4
798	NISSAN	370Z	TWO-SEATER	3.7	6	7.0	Z	12.6	9.3	11.1	25	261	4

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Interactions

Correlations

Missing values

Sample

Duplicate rows

Most frequently occurring